API Reference

This section contains detailed API documentation for all MLAI modules and functions.

API Reference:

• MLAI Core Module
• Plotting Module
  • mlai.plot
  • pred_range()
  • matrix()
  • base_plot()
  • covariance_capacity()
  • prob_diagram()
  • bernoulli_urn()
  • bayes_billiard()
  • hyperplane_coordinates()
  • init_perceptron_plot()
  • update_perceptron_plot()
  • contour_error()
  • regression_contour()
  • init_regression()
  • update_regression()
  • update_regression_path()
  • regression_contour_fit()
  • regression_contour_sgd()
  • regression_contour_coordinate_descent()
  • over_determined_system()
  • gaussian_of_height()
  • gaussian_volume_1D()
  • gaussian_volume_2D()
  • gaussian_volume_3D()
  • marathon_fit()
  • rmse_fit()
  • holdout_fit()
  • loo_fit()
  • cv_fit()
  • under_determined_system()
  • bayes_update()
  • height_weight()
  • independent_height_weight()
  • correlated_height_weight()
  • two_point_pred()
  • output_augment_x()
  • basis()
  • computing_covariance()
  • kern_circular_sample()
  • animate_covariance_function()
  • multi_output_covariance_func()
  • multi_output_covariance_heatmap()
  • multi_output_sample_plot()
  • multi_output_animate_covariance_function()
  • covariance_func()
  • rejection_samples()
  • two_point_sample()
  • poisson()
  • logistic()
  • height()
  • weight()
  • low_rank_approximation()
  • blank_canvas()
  • kronecker_illustrate()
  • kronecker_IK()
  • kronecker_IK_highlight()
  • kronecker_WX()
  • perceptron()
  • dist2()
  • clear_axes()
  • non_linear_difficulty_plot_3()
  • non_linear_difficulty_plot_2()
  • non_linear_difficulty_plot_1()
  • network
  • layer
  • neural_network()
  • deep_nn()
  • deep_nn_bottleneck()
  • box()
  • stack_gp_sample()
  • vertical_chain()
  • horizontal_chain()
  • shared_gplvm()
  • ppca_graphical_model()
  • dppca_graphical_model()
  • three_pillars_innovation()
  • model_output()
  • model_sample()
  • multiple_optima()
  • google_trends()
  • gp_optimize_quadratic()
  • tsne_example()
  • squared_distances()
  • visualise_relu_activations()
  • visualise_activation_summary()
  • visualise_decision_boundaries()
• Gaussian Process Tutorial Module
  • ax_default()
  • meanplot()
  • gpplot()
• Deep Gaussian Process Tutorial Module
• Mountain Car Module

Module Overview

• mlai: Core machine learning functionality

• plot: Plotting utilities and visualization tools

• gp_tutorial: Gaussian Process tutorial functions

• deepgp_tutorial: Deep Gaussian Process tutorial functions

• mountain_car: Mountain car reinforcement learning example

Code Style

All MLAI code follows these conventions:

• Functions and classes are documented with detailed docstrings

• Mathematical notation is used in comments and docstrings

• Type hints are provided where they improve clarity

• Examples are included in docstrings

For more information about the project’s coding standards, see the Project Tenets.